The Specificity of Algorithms

Algorithms imply a precise specification,  and when I see one I always want a close specification of context and derivation.

Want Less-Biased Decisions? Use Algorithms.   by Alex P. Miller in HBR

A quiet revolution is taking place. In contrast to much of the press coverage of artificial intelligence, this revolution is not about the ascendance of a sentient android army. Rather, it is characterized by a steady increase in the automation of traditionally human-based decision processes throughout organizations all over the country. While advancements like AlphaGo Zero make for catchy headlines, it is fairly conventional machine learning and statistical techniques — ordinary least squares, logistic regression, decision trees — that are adding real value to the bottom line of many organizations. Real-world applications range from medical diagnoses and judicial sentencing to professional recruiting and resource allocation in public agencies.

Is this revolution a good thing? There seems to be a growing cadre of authors, academics, and journalists that would answer in the negative. Book titles in this genre include Weapons of Math Destruction, Automating Inequality, and The Black Box Society. There has also been a spate of exposé-style longform articles such as “Machine Bias,” “Austerity Is an Algorithm,” and “Are Algorithms Building the New Infrastructure of Racism?” At the heart of this work is the concern that algorithms are often opaque, biased, and unaccountable tools being wielded in the interests of institutional power. So how worried should we be about the modern ascendance of algorithms? ... "